Video Assessment to Detect Amyotrophic Lateral Sclerosis
Abstract: Introduction: Weakened facial movements are early-stage symptoms of amyotrophic lateral sclerosis (ALS). ALS is generally detected based on changes in facial expressions, but large differences between individuals can lead to subjectivity in the diagnosis. We have proposed a computerized analysis of facial expression videos to detect ALS. Methods: This study investigated the action units obtained from facial expression videos to differentiate between ALS patients and healthy individuals, identifying the specific action units and facial expressions that give the best results. We utilized the Toronto NeuroFace Dataset, which includes nine facial expression tasks for healthy individuals and ALS patients. Results: The best classification accuracy was 0.91 obtained for the pretending to smile with tight lips expression. Conclusion: This pilot study shows the potential of using computerized facial expression analysis based on action units to identify facial weakness symptoms in ALS.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Video Assessment to Detect Amyotrophic Lateral Sclerosis ; volume:8 ; number:1 ; year:2024 ; pages:171-180 ; extent:10
Digital biomarkers ; 8, Heft 1 (2024), 171-180 (gesamt 10)
- Urheber
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Oliveira, Guilherme Camargo
Ngo, Quoc Cuong
Passos, Leandro Aparecido
Oliveira, Leonardo Silva
Stylianou, Stella
Papa, João Paulo
Kumar, Dinesh
- DOI
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10.1159/000540547
- URN
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urn:nbn:de:101:1-2412260049372.306470719339
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:33 MESZ
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Beteiligte
- Oliveira, Guilherme Camargo
- Ngo, Quoc Cuong
- Passos, Leandro Aparecido
- Oliveira, Leonardo Silva
- Stylianou, Stella
- Papa, João Paulo
- Kumar, Dinesh
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